Results Notebook (no outliers)

1 Overview

1.1 Data inspection

1.2 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 790) ranged in age 18 to 79 (Mdn_age = 35.00, M_age = 37.35, SD = 13.54). The participants were predominantly female (56.84%). The participants received £0.35 ($0.45) for successfully completing the task.

1.3 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Table 1.1: Randomisation check

Item	Dynamic	Static	No norm	Significance test
Age (years)	37.65 \(\pm\) 14.19	38.16 \(\pm\) 13.00	36.24 \(\pm\) 13.41	\(F(2, 787) = 1.44\), \(\mathit{MSE} = 183.14\), \(p = .238\)
Gender (%)	Male (40.23%) Female (59.38%) Other (0.39%)	Male (42.16%) Female (57.84%)	Male (46.62%) Female (53.38%)	\(\chi^2(4, n = 790) = 4.29\), \(p = .368\)
Political position	3.50 \(\pm\) 1.22	3.54 \(\pm\) 1.26	3.45 \(\pm\) 1.28	\(F(2, 787) = 0.34\), \(\mathit{MSE} = 1.57\), \(p = .709\)
Nationality (%)	1 (85.16%) 2 (3.12%) 3 (9.77%) 4 (0.78%) NA (1.17%)	1 (80.6%) 2 (5.6%) 3 (8.58%) 4 (1.49%) NA (3.73%)	1 (80.08%) 2 (5.64%) 3 (9.4%) 4 (1.88%) NA (3.01%)	\(\chi^2(6, n = 790) = 3.98\), \(p = .680\)

1.4 Correlations

Table 1.2: Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures

	Alpha	M	SD	1	2	3	4	5	6
Interest		3.54	1.80
Attitude	0.91	4.64	1.26	.82**
Intention	0.98	4.22	1.77	.84**	.84**
Expectation	0.98	3.92	1.72	.81**	.82**	.92**
Intent/expectation composite		4.07	1.71	.84**	.85**	.98**	.98**
Perception of change		5.18	0.78	.36**	.35**	.35**	.34**	.35**
Preconformity		4.14	1.15	.43**	.40**	.39**	.36**	.39**	.46**

2 Confirmatory analyses

2.1 Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing meat consumption (compared to static norm salience)?

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from M_diff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

Figure 2.1: World cloud of participants response to text

Table 2.1:

Most frequent words in text

word	freq
health	210.00
environment	181.00
animals	136.00
climate	61.00
awareness	60.00
concerns	60.00
impact	50.00
media	50.00
vegan	38.00
better	31.00

The mean interest for participants in the dynamic norm condition was M = 3.60 (SD = 1.83), and the mean interest in the static norm condition was M = 3.63 (SD = 1.83). The mean interest in the no norm condition was M = 3.39 (SD = 1.73).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.60, SD = 1.83) and static norm (M = 3.63, SD = 1.83) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.28]\), \(t(787) = -0.18\), \(p = .854\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.19, RR[0.69, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.39, SD = 1.73) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.21\), 95% CI \([-0.10\), \(0.52]\), \(t(787) = 1.31\), \(p = .190\), d = 0.12, or the static-norm condition \(\Delta M = 0.24\), 95% CI \([-0.07\), \(0.54]\), \(t(787) = 1.51\), \(p = .130\), d = 0.13. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.09\), 95% CI \([-0.18\), \(0.36]\), \(t(787) = 0.65\), \(p = .516\).

Table 2.2:

Meat consumption by condition contrasts

	contrast	\(\Delta M\)	95% CI	\(t(787)\)	\(p\)
DYST	Dynamic, static	-0.03	\([-0.34\), \(0.28]\)	-0.18	.854
DYNO	Dynamic, control	0.21	\([-0.10\), \(0.52]\)	1.31	.190
STNO	Static, control	0.24	\([-0.07\), \(0.54]\)	1.51	.130
DYCONT	Dynamic, both	0.09	\([-0.18\), \(0.36]\)	0.65	.516
EXPNO	Norms, control	-0.22	\([-0.49\), \(0.04]\)	-1.63	.103

2.2 Will participants in the dynamic norm condition be more likely (than those in the static norm control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of M_diff = 0.40. The plausible maximum effect was set at twice the predicted effect of M_diff = 0.80. A Bayes factor was calculated for each test.

Table 2.3: Expectations of future meat consumption

		Future Norm		Preconformity		Combined
Condition	\(n\)	\(M\)	\(SD\)	\(M\)	\(SD\)	\(M\)	\(SD\)
Dynamic	256	5.32	0.79	4.30	1.14	4.81	0.85
Static	268	5.20	0.76	4.19	1.16	4.70	0.83
No norm	266	5.02	0.77	3.92	1.13	4.47	0.79

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.12\), 95% CI \([-0.01\), \(0.25]\), \(t(787) = 1.76\), \(p = .079\), d = 0.15, \(B_{\text{HN}(0, 0.40)}\) = 1.43, RR[0.05, 2]

Table 2.4: Perception change contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.12	\([-0.01\), \(0.25]\)	1.76	.079
DYNO	Dynamic, control	0.29	\([0.16\), \(0.43]\)	4.34	< .001
STNO	Static, control	0.18	\([0.04\), \(0.31]\)	2.62	.009
DYCONT	Dynamic, both	0.21	\([0.09\), \(0.32]\)	3.51	< .001
EXPNO	Norms, control	-0.23	\([-0.35\), \(-0.12]\)	-4.03	< .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.30, SD = 1.14) and static norm (M = 4.19, SD = 1.16) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(787) = 1.11\), \(p = .269\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.74, RR[0.05, 1.55].

Table 2.5: Preconformity contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.11	\([-0.09\), \(0.31]\)	1.11	.269
DYNO	Dynamic, control	0.38	\([0.19\), \(0.58]\)	3.83	< .001
STNO	Static, control	0.27	\([0.08\), \(0.47]\)	2.75	.006
DYCONT	Dynamic, both	0.25	\([0.08\), \(0.42]\)	2.84	.005
EXPNO	Norms, control	-0.33	\([-0.50\), \(-0.16]\)	-3.81	< .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.85) and static norm (M = 4.70, SD = 0.83) conditions, \(\Delta M = 0.11\), 95% CI \([-0.03\), \(0.26]\), \(t(787) = 1.59\), \(p = .111\), d = 0.14, \(B_{\text{HN}(0, 0.40)}\) = 1.14, RR[0.05, 2].

Table 2.6: Combined contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.11	\([-0.03\), \(0.26]\)	1.59	.111
DYNO	Dynamic, control	0.34	\([0.20\), \(0.48]\)	4.70	< .001
STNO	Static, control	0.22	\([0.08\), \(0.36]\)	3.15	.002
DYCONT	Dynamic, both	0.23	\([0.10\), \(0.35]\)	3.63	< .001
EXPNO	Norms, control	-0.28	\([-0.40\), \(-0.16]\)	-4.54	< .001

3 Secondary analyses

3.1 Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 130.379 p-value lower bound: 0e+00 t-value upper bound: \(-33.392\) p-value upper bound: \(6.46\times10^{-132}\) degrees of freedom : 521.6

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 2.86 upper bound 90% CI: 3.062

NHST confidence interval: lower bound 95% CI: 2.841 upper bound 95% CI: 3.081

Equivalence Test Result: The equivalence test was significant, t(521.6) = \(-33.392\), p = \(6.46\times10^{-132}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(521.6) = 48.493, p = \(2.11\times10^{-195}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.2 Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 14.613 p-value lower bound: \(4.35\times10^{-41}\) t-value upper bound: \(-13.059\) p-value upper bound: \(3.16\times10^{-34}\) degrees of freedom : 521.78

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.315\) upper bound 90% CI: 0.876

NHST confidence interval: lower bound 95% CI: \(-0.429\) upper bound 95% CI: 0.991

Equivalence Test Result: The equivalence test was significant, t(521.78) = \(-13.059\), p = \(3.16\times10^{-34}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(521.78) = 0.777, p = .438, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory analyses

4.1 Does dynamic norm (versus static norm) information lead to more positive attitudes, intentions, and expectations to reduce meat consumption?

4.1.1 Trace plots

Figure 4.1: Traceplots of regression parameters

Figure 4.2: Traceplots of regression parameters

Figure 4.3: Traceplots of regression parameters

4.1.2 Posterior plots

Figure 4.4: Posterior uncertainty intervals

Figure 4.5: Posterior density plot

4.1.3 Summary table

Table 4.1: Posterior results for simple model (H3)

	Model 1: Uninformative priors\(^a\)					Model 2: Weakly informative priors\(^b\)						Model 3: Moderately informative priors\(^c\)
	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Interest	0.035 (0.154)	-0.268, 0.335	738.292	1	normal(0,10)	0.088 (0.163)	-0.237, 0.405	708.044	1	151.43	normal(0.5,0.75)	0.204 (0.135)	-0.059, 0.479	757.104	1.003	482.86	normal(0.5, 0.35)
Attitude	-0.036 (0.108)	-0.249, 0.174	750.076	1.001	normal(0,10)	-0.000 (0.115)	-0.226, 0.223	728.624	1	-100	normal(0.5,0.75)	0.082 (0.098)	-0.11, 0.273	727.912	1.005	-327.78	normal(0.5, 0.35)
Intention/Expectation	-0.024 (0.143)	-0.302, 0.257	721.838	1.001	normal(0,10)	0.025 (0.154)	-0.273, 0.339	705.427	1	-204.17	normal(0.5,0.75)	0.136 (0.127)	-0.118, 0.389	716.604	1.003	-666.67	normal(0.5, 0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .504 b ppp = .493 c ppp = .451

4.2 Does age interact with norm condition to influence dependent variables?

4.2.1 Trace plots

Figure 4.6: Traceplots of regression parameters

Figure 4.7: Traceplots of regression parameters

Figure 4.8: Traceplots of regression parameters

4.2.2 Posterior plots

Figure 4.9: Posterior uncertainty intervals

4.2.3 Summary table

Table 4.2: Posterior results for multi-sample analysis by age (H4)

	Model 1: Uninformative priors\(^a\)					Model 2: Informative priors\(^b\)						Model 3: Informative priors\(^c\)
Parameter	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Young adults
Interest	0.223 (0.332)	-0.411, 0.864	1468.645	1.004	normal(0,10)	0.349 (0.285)	-0.208, 0.904	1080.827	1	56.5	normal(0.5,0.75)	0.484 (0.213)	0.066, 0.9	1543.916	1.001	117.04	normal(0.5,0.35)
Attitude	0.147 (0.236)	-0.322, 0.611	1463.529	1.002	normal(0,10)	0.238 (0.202)	-0.151, 0.647	1175.109	1.001	61.9	normal(0.5,0.75)	0.342 (0.165)	0.013, 0.667	1247.571	1.004	132.65	normal(0.5,0.35)
Intention/Expectation	0.046 (0.308)	-0.544, 0.655	1394.423	1.005	normal(0,10)	0.168 (0.258)	-0.329, 0.671	1045.390	1.001	265.22	normal(0.5,0.75)	0.307 (0.197)	-0.078, 0.683	979.732	1.005	567.39	normal(0.5,0.35)
Middle-aged adults
Interest	0.027 (0.233)	-0.439, 0.488	1280.253	1.002	normal(0,10)	0.139 (0.216)	-0.27, 0.572	1009.412	1.006	414.81	normal(0.5,0.75)	0.314 (0.171)	-0.023, 0.661	1188.646	1.003	1062.96	normal(0.5,0.35)
Attitude	-0.106 (0.168)	-0.438, 0.229	1210.094	1.002	normal(0,10)	-0.024 (0.159)	-0.334, 0.297	1033.752	1.008	-77.36	normal(0.5,0.75)	0.109 (0.128)	-0.131, 0.365	1133.668	1.004	-202.83	normal(0.5,0.35)
Intention/Expectation	-0.042 (0.223)	-0.495, 0.393	1344.973	1.002	normal(0,10)	0.069 (0.209)	-0.347, 0.484	962.467	1.007	-264.29	normal(0.5,0.75)	0.243 (0.166)	-0.078, 0.574	1173.682	1.004	-678.57	normal(0.5,0.35)
Old adults
Interest	-0.107 (0.283)	-0.664, 0.444	1024.560	1.002	normal(0,10)	0.042 (0.254)	-0.452, 0.529	872.720	1.007	-139.25	normal(0.5,0.75)	0.265 (0.185)	-0.096, 0.628	979.422	1.002	-347.66	normal(0.5,0.35)
Attitude	-0.073 (0.190)	-0.445, 0.306	1036.491	1.002	normal(0,10)	0.024 (0.175)	-0.321, 0.37	919.469	1.006	-132.88	normal(0.5,0.75)	0.175 (0.136)	-0.093, 0.446	930.504	1.001	-339.73	normal(0.5,0.35)
Intention/Expectation	-0.063 (0.260)	-0.581, 0.448	1122.595	1.002	normal(0,10)	0.075 (0.233)	-0.383, 0.537	897.787	1.006	-219.05	normal(0.5,0.75)	0.275 (0.178)	-0.063, 0.631	939.502	1.002	-536.51	normal(0.5,0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .488 b ppp = .485 c ppp = .429

Table 4.3: Posterior results for moderation model using age as continuous variable (H4)

	Estimate	Post.SD	pi.lower	pi.upper	Rhat	neff	Prior
Interest
conditionbi	0.049	0.160	-0.265	0.354	1.005	720.555	normal(0,10)
age_cent	-0.018	0.009	-0.036	-0.001	1.002	1194.944	normal(0,10)
condition_age	0.010	0.012	-0.014	0.032	1.002	1105.091	normal(0,10)
Attitudes
conditionbi	-0.031	0.114	-0.255	0.196	1.006	732.785	normal(0,10)
age_cent	-0.007	0.006	-0.02	0.005	1.001	1218.820	normal(0,10)
condition_age	0.005	0.008	-0.012	0.021	1.001	1111.234	normal(0,10)
Intentions/Expectations
conditionbi	-0.026	0.148	-0.315	0.26	1.007	719.482	normal(0,10)
age_cent	-0.001	0.008	-0.017	0.014	1.002	1111.115	normal(0,10)
condition_age	0.000	0.011	-0.022	0.021	1.002	1070.955	normal(0,10)

4.3 Do demographic factors such as age, gender, and political position predict the primary dependent variables?

4.3.1 Trace plots

Figure 4.10: Traceplots for estimated regression parameters

Figure 4.11: Traceplots for estimated regression parameters

Figure 4.12: Traceplots for estimated regression parameters

4.3.2 Posterior plots

Figure 4.13: Posterior uncertainty intervals

Figure 4.14: Posterior density plot

4.3.3 Summary table

Table 4.4: Posterior results for full model (H5)

	Model 1: Uninformative priors\(^a\)					Model 2: Informative priors\(^b\)						Model 3: Informative priors\(^c\)
Parameter	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Interest
Condition	0.042 (0.153)	-0.261, 0.338	1326.839	1.002	normal(0,10)	0.088 (0.148)	-0.198, 0.38	1482.906	1.001	109.52	normal(0.5,0.75)	0.207 (0.134)	-0.056, 0.478	1444.273	1.006	392.86	normal(0.5,0.35)
Age	-0.006 (0.006)	-0.017, 0.006	2207.291	1	normal(0,10)	-0.006 (0.006)	-0.018, 0.005	2397.144	1	0	normal(0,10)	-0.006 (0.006)	-0.016, 0.006	2415.903	0.999	0	normal(0,10)
Gender	0.491 (0.158)	0.187, 0.787	1391.653	1.002	normal(0,10)	0.499 (0.154)	0.201, 0.796	1074.244	1.001	1.63	normal(0,10)	0.493 (0.162)	0.184, 0.813	1179.136	1.001	0.41	normal(0,10)
Politics	-0.286 (0.065)	-0.416, -0.156	1437.685	1.001	normal(0,10)	-0.289 (0.063)	-0.413, -0.164	1414.559	1	1.05	normal(0,10)	-0.289 (0.063)	-0.418, -0.162	1445.607	1	1.05	normal(0,10)
Attitudes
Condition	-0.034 (0.106)	-0.237, 0.167	1406.600	1.002	normal(0,10)	-0.002 (0.104)	-0.207, 0.199	1354.936	1.002	-94.12	normal(0.5,0.75)	0.082 (0.097)	-0.113, 0.273	1493.845	1.006	-341.18	normal(0.5,0.35)
Age	0.001 (0.004)	-0.008, 0.009	2317.231	1	normal(0,10)	0.001 (0.004)	-0.008, 0.008	2343.693	1	0	normal(0,10)	0.001 (0.004)	-0.007, 0.009	2431.615	0.999	0	normal(0,10)
Gender	0.308 (0.113)	0.09, 0.522	1391.416	1.002	normal(0,10)	0.313 (0.109)	0.102, 0.527	959.334	1.001	1.62	normal(0,10)	0.308 (0.115)	0.087, 0.527	1061.106	1	0	normal(0,10)
Politics	-0.238 (0.046)	-0.327, -0.149	1454.913	1	normal(0,10)	-0.239 (0.045)	-0.328, -0.153	1352.135	1.001	0.42	normal(0,10)	-0.239 (0.045)	-0.324, -0.149	1431.927	1.001	0.42	normal(0,10)
Intention/Expectations
Condition	-0.021 (0.143)	-0.309, 0.252	1404.500	1.004	normal(0,10)	0.020 (0.139)	-0.248, 0.292	1363.134	1.002	-195.24	normal(0.5,0.75)	0.134 (0.128)	-0.113, 0.384	1458.703	1.005	-738.1	normal(0.5,0.35)
Age	0.006 (0.006)	-0.005, 0.016	2133.728	1	normal(0,10)	0.005 (0.006)	-0.006, 0.016	2250.153	1	-16.67	normal(0,10)	0.006 (0.005)	-0.005, 0.016	2527.464	1	0	normal(0,10)
Gender	0.603 (0.151)	0.307, 0.898	1422.070	1.003	normal(0,10)	0.610 (0.145)	0.326, 0.895	1058.218	1.001	1.16	normal(0,10)	0.602 (0.154)	0.305, 0.899	1118.308	1.001	-0.17	normal(0,10)
Politics	-0.258 (0.061)	-0.379, -0.143	1346.709	1	normal(0,10)	-0.259 (0.060)	-0.38, -0.14	1328.150	1	0.39	normal(0,10)	-0.260 (0.058)	-0.376, -0.141	1378.480	1	0.78	normal(0,10)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .501 b ppp = .497 c ppp = .450

5 Unregistered analyses

5.1 Power test

Sparkman and Walton (2017) found standardized effects of dynamic norms on interest in reducing meat consumption ranging from \(d\) = 0.31 – 0.41. To detect the average effect of \(d\) = 0.36, we would need 122 participants in each condition.

 Two-sample t test power calculation 

          n = 122.0922
          d = 0.36
  sig.level = 0.05
      power = 0.8
alternative = two.sided

NOTE: n is number in each group

5.2 Moderation of demographic variables

Table 5.1:

Exploring the effect moderation of demographic variables on attitudes

Predictor	\(b\)	95% CI	\(t(517)\)	\(p\)
Intercept	5.40	\([4.88\), \(5.92]\)	20.31	< .001
ConditionbiStatic	-0.16	\([-0.88\), \(0.56]\)	-0.44	.658
GenderbiFemale	0.39	\([0.08\), \(0.71]\)	2.44	.015
POLITICS	-0.27	\([-0.40\), \(-0.15]\)	-4.21	< .001
ConditionbiStatic \(\times\) GenderbiFemale	-0.17	\([-0.61\), \(0.27]\)	-0.78	.438
ConditionbiStatic \(\times\) POLITICS	0.07	\([-0.11\), \(0.24]\)	0.74	.462

Table 5.2:

Exploring the effect moderation of demographic variables on intention

Predictor	\(b\)	95% CI	\(t(517)\)	\(p\)
Intercept	4.73	\([4.04\), \(5.42]\)	13.41	< .001
ConditionbiStatic	-0.23	\([-1.19\), \(0.72]\)	-0.48	.632
GenderbiFemale	0.80	\([0.38\), \(1.22]\)	3.76	< .001
POLITICS	-0.32	\([-0.49\), \(-0.15]\)	-3.70	< .001
ConditionbiStatic \(\times\) GenderbiFemale	-0.43	\([-1.01\), \(0.16]\)	-1.43	.152
ConditionbiStatic \(\times\) POLITICS	0.13	\([-0.10\), \(0.36]\)	1.11	.266

Table 5.3:

Exploring the effect moderation of demographic variables on interest

predictor	\(b\)	95% CI	\(t(517)\)	\(p\)
Intercept	4.53	\([3.79\), \(5.27]\)	12.05	< .001
Condition	-0.28	\([-1.30\), \(0.74]\)	-0.54	.589
Gender	0.54	\([0.09\), \(0.98]\)	2.36	.019
Political position	-0.35	\([-0.53\), \(-0.17]\)	-3.87	< .001
Condition \(\times\) Gender	-0.08	\([-0.70\), \(0.54]\)	-0.25	.803
Condition \(\times\) Political position	0.10	\([-0.14\), \(0.35]\)	0.83	.408

5.3 Mediation

Table 5.4: Exploring mediating effect of attitude on intentions/expectations

Parameter	\(M\)	\(SD\)	Lower PPI	Upper PPI	Rhat	Prior
Direct effects
Interest ~ Condition	0.028	0.160	-0.294	0.335	1.001	normal(0,10)
Attitude ~ Condition	-0.041	0.109	-0.255	0.175	1.002	normal(0,10)
Intent/Expectation ~ Condition	-0.005	0.097	-0.198	0.181	1.001	normal(0,10)
Intent/Expectation ~ Attitude	0.630	0.047	0.533	0.719	1.002	normal(0,10)
Mediation
Indirect effects/nCondition > Attitude > Intention/Expectation	-0.026	0.069	-0.161	0.11	NA
Total effect	-0.030	0.119	-0.264	0.203	NA

6 Environment and data

6.1 Session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.1.0 (2021-05-18)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-07-19                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                               
##  abind          1.4-5      2016-07-21 [1] CRAN (R 4.1.0)                       
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.1.0)                       
##  backports      1.2.1      2020-12-09 [1] CRAN (R 4.1.0)                       
##  bayesplot      1.8.1      2021-06-14 [1] CRAN (R 4.1.0)                       
##  bfrr         * 0.0.0.9000 2021-06-29 [1] Github (debruine/bfrr@9b80a99)       
##  bitops         1.0-7      2021-04-24 [1] CRAN (R 4.1.0)                       
##  bookdown       0.22       2021-04-22 [1] CRAN (R 4.1.0)                       
##  boot           1.3-28     2021-05-03 [1] CRAN (R 4.1.0)                       
##  broom          0.7.8      2021-06-24 [1] CRAN (R 4.1.0)                       
##  bslib          0.2.5.1    2021-05-18 [1] CRAN (R 4.1.0)                       
##  cachem         1.0.5      2021-05-15 [1] CRAN (R 4.1.0)                       
##  callr          3.7.0      2021-04-20 [1] CRAN (R 4.1.0)                       
##  car            3.0-11     2021-06-27 [1] CRAN (R 4.1.0)                       
##  carData        3.0-4      2020-05-22 [1] CRAN (R 4.1.0)                       
##  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.1.0)                       
##  cli            3.0.1      2021-07-17 [1] CRAN (R 4.1.0)                       
##  coda           0.19-4     2020-09-30 [1] CRAN (R 4.1.0)                       
##  codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.1.0)                       
##  colorspace     2.0-2      2021-06-24 [1] CRAN (R 4.1.0)                       
##  crayon         1.4.1      2021-02-08 [1] CRAN (R 4.1.0)                       
##  curl           4.3.2      2021-06-23 [1] CRAN (R 4.1.0)                       
##  data.table     1.14.0     2021-02-21 [1] CRAN (R 4.1.0)                       
##  DBI            1.1.1      2021-01-15 [1] CRAN (R 4.1.0)                       
##  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.1.0)                       
##  desc           1.3.0      2021-03-05 [1] CRAN (R 4.1.0)                       
##  devtools       2.4.2      2021-06-07 [1] CRAN (R 4.1.0)                       
##  digest         0.6.27     2020-10-24 [1] CRAN (R 4.1.0)                       
##  dplyr        * 1.0.7      2021-06-18 [1] CRAN (R 4.1.0)                       
##  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.1.0)                       
##  emmeans      * 1.6.2-1    2021-07-08 [1] CRAN (R 4.1.0)                       
##  estimability   1.3        2018-02-11 [1] CRAN (R 4.1.0)                       
##  evaluate       0.14       2019-05-28 [1] CRAN (R 4.1.0)                       
##  ez             4.4-0      2016-11-02 [1] CRAN (R 4.1.0)                       
##  fansi          0.5.0      2021-05-25 [1] CRAN (R 4.1.0)                       
##  farver         2.1.0      2021-02-28 [1] CRAN (R 4.1.0)                       
##  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.1.0)                       
##  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.1.0)                       
##  foreign        0.8-81     2020-12-22 [1] CRAN (R 4.1.0)                       
##  fs             1.5.0      2020-07-31 [1] CRAN (R 4.1.0)                       
##  generics       0.1.0      2020-10-31 [1] CRAN (R 4.1.0)                       
##  ggplot2      * 3.3.5      2021-06-25 [1] CRAN (R 4.1.0)                       
##  ggridges       0.5.3      2021-01-08 [1] CRAN (R 4.1.0)                       
##  glue           1.4.2      2020-08-27 [1] CRAN (R 4.1.0)                       
##  gridExtra      2.3        2017-09-09 [1] CRAN (R 4.1.0)                       
##  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.1.0)                       
##  haven          2.4.1      2021-04-23 [1] CRAN (R 4.1.0)                       
##  here         * 1.0.1      2020-12-13 [1] CRAN (R 4.1.0)                       
##  highr          0.9        2021-04-16 [1] CRAN (R 4.1.0)                       
##  hms            1.1.0      2021-05-17 [1] CRAN (R 4.1.0)                       
##  htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.1.0)                       
##  httr           1.4.2      2020-07-20 [1] CRAN (R 4.1.0)                       
##  insight        0.14.2     2021-06-22 [1] CRAN (R 4.1.0)                       
##  jquerylib      0.1.4      2021-04-26 [1] CRAN (R 4.1.0)                       
##  jsonlite       1.7.2      2020-12-09 [1] CRAN (R 4.1.0)                       
##  kableExtra   * 1.3.4.9000 2021-07-03 [1] Github (haozhu233/kableExtra@4c93f1a)
##  knitr        * 1.33       2021-04-24 [1] CRAN (R 4.1.0)                       
##  labeling       0.4.2      2020-10-20 [1] CRAN (R 4.1.0)                       
##  labelled       2.8.0      2021-03-08 [1] CRAN (R 4.1.0)                       
##  lattice        0.20-44    2021-05-02 [1] CRAN (R 4.1.0)                       
##  lifecycle      1.0.0      2021-02-15 [1] CRAN (R 4.1.0)                       
##  lme4           1.1-27.1   2021-06-22 [1] CRAN (R 4.1.0)                       
##  lubridate      1.7.10     2021-02-26 [1] CRAN (R 4.1.0)                       
##  magrittr       2.0.1      2020-11-17 [1] CRAN (R 4.1.0)                       
##  MASS         * 7.3-54     2021-05-03 [1] CRAN (R 4.1.0)                       
##  Matrix         1.3-4      2021-06-01 [1] CRAN (R 4.1.0)                       
##  MBESS          4.8.0      2020-08-05 [1] CRAN (R 4.1.0)                       
##  memoise        2.0.0      2021-01-26 [1] CRAN (R 4.1.0)                       
##  mgcv           1.8-36     2021-06-01 [1] CRAN (R 4.1.0)                       
##  minqa          1.2.4      2014-10-09 [1] CRAN (R 4.1.0)                       
##  mnormt         2.0.2      2020-09-01 [1] CRAN (R 4.1.0)                       
##  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.1.0)                       
##  MOTE         * 1.0.2      2019-04-10 [1] CRAN (R 4.1.0)                       
##  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.1.0)                       
##  mvtnorm        1.1-2      2021-06-07 [1] CRAN (R 4.1.0)                       
##  nlme           3.1-152    2021-02-04 [1] CRAN (R 4.1.0)                       
##  nloptr         1.2.2.2    2020-07-02 [1] CRAN (R 4.1.0)                       
##  NLP          * 0.2-1      2020-10-14 [1] CRAN (R 4.1.0)                       
##  openxlsx       4.2.4      2021-06-16 [1] CRAN (R 4.1.0)                       
##  papaja       * 0.1.0.9997 2021-06-11 [1] Github (crsh/papaja@a231c36)         
##  pillar         1.6.1      2021-05-16 [1] CRAN (R 4.1.0)                       
##  pkgbuild       1.2.0      2020-12-15 [1] CRAN (R 4.1.0)                       
##  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.1.0)                       
##  pkgload        1.2.1      2021-04-06 [1] CRAN (R 4.1.0)                       
##  plyr           1.8.6      2020-03-03 [1] CRAN (R 4.1.0)                       
##  prettyunits    1.1.1      2020-01-24 [1] CRAN (R 4.1.0)                       
##  processx       3.5.2      2021-04-30 [1] CRAN (R 4.1.0)                       
##  ps             1.6.0      2021-02-28 [1] CRAN (R 4.1.0)                       
##  psy          * 1.1        2012-06-21 [1] CRAN (R 4.1.0)                       
##  psych        * 2.1.6      2021-06-18 [1] CRAN (R 4.1.0)                       
##  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.1.0)                       
##  qualtRics    * 3.1.4      2021-01-14 [1] CRAN (R 4.1.0)                       
##  R6             2.5.0      2020-10-28 [1] CRAN (R 4.1.0)                       
##  RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.1.0)                       
##  Rcpp         * 1.0.7      2021-07-07 [1] CRAN (R 4.1.0)                       
##  RCurl        * 1.98-1.3   2021-03-16 [1] CRAN (R 4.1.0)                       
##  readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.1.0)                       
##  readxl         1.3.1      2019-03-13 [1] CRAN (R 4.1.0)                       
##  remotes        2.4.0      2021-06-02 [1] CRAN (R 4.1.0)                       
##  reprex         2.0.0      2021-04-02 [1] CRAN (R 4.1.0)                       
##  reshape        0.8.8      2018-10-23 [1] CRAN (R 4.1.0)                       
##  reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.1.0)                       
##  rio            0.5.27     2021-06-21 [1] CRAN (R 4.1.0)                       
##  rlang        * 0.4.11     2021-04-30 [1] CRAN (R 4.1.0)                       
##  rmarkdown      2.9        2021-06-15 [1] CRAN (R 4.1.0)                       
##  rprojroot      2.0.2      2020-11-15 [1] CRAN (R 4.1.0)                       
##  rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.1.0)                       
##  rvest          1.0.0      2021-03-09 [1] CRAN (R 4.1.0)                       
##  sass           0.4.0      2021-05-12 [1] CRAN (R 4.1.0)                       
##  scales         1.1.1      2020-05-11 [1] CRAN (R 4.1.0)                       
##  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.1.0)                       
##  sjlabelled     1.1.8      2021-05-11 [1] CRAN (R 4.1.0)                       
##  slam           0.1-48     2020-12-03 [1] CRAN (R 4.1.0)                       
##  SnowballC    * 0.7.0      2020-04-01 [1] CRAN (R 4.1.0)                       
##  stringi        1.7.3      2021-07-16 [1] CRAN (R 4.1.0)                       
##  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.1.0)                       
##  svglite        2.0.0      2021-02-20 [1] CRAN (R 4.1.0)                       
##  systemfonts    1.0.2      2021-05-11 [1] CRAN (R 4.1.0)                       
##  testthat       3.0.4      2021-07-01 [1] CRAN (R 4.1.0)                       
##  tibble       * 3.1.2      2021-05-16 [1] CRAN (R 4.1.0)                       
##  tidyr        * 1.1.3      2021-03-03 [1] CRAN (R 4.1.0)                       
##  tidyselect     1.1.1      2021-04-30 [1] CRAN (R 4.1.0)                       
##  tidyverse    * 1.3.1      2021-04-15 [1] CRAN (R 4.1.0)                       
##  tm           * 0.7-8      2020-11-18 [1] CRAN (R 4.1.0)                       
##  tmvnsim        1.0-2      2016-12-15 [1] CRAN (R 4.1.0)                       
##  TOSTER       * 0.3.4      2018-08-03 [1] CRAN (R 4.1.0)                       
##  usethis        2.0.1      2021-02-10 [1] CRAN (R 4.1.0)                       
##  utf8           1.2.1      2021-03-12 [1] CRAN (R 4.1.0)                       
##  vctrs          0.3.8      2021-04-29 [1] CRAN (R 4.1.0)                       
##  viridisLite    0.4.0      2021-04-13 [1] CRAN (R 4.1.0)                       
##  webshot        0.5.2      2019-11-22 [1] CRAN (R 4.1.0)                       
##  withr          2.4.2      2021-04-18 [1] CRAN (R 4.1.0)                       
##  wordcloud    * 2.6        2018-08-24 [1] CRAN (R 4.1.0)                       
##  xfun           0.24       2021-06-15 [1] CRAN (R 4.1.0)                       
##  XML          * 3.99-0.6   2021-03-16 [1] CRAN (R 4.1.0)                       
##  xml2           1.3.2      2020-04-23 [1] CRAN (R 4.1.0)                       
##  xtable         1.8-4      2019-04-21 [1] CRAN (R 4.1.0)                       
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.1.0)                       
##  zip            2.2.0      2021-05-31 [1] CRAN (R 4.1.0)                       
## 
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

6.2 Codebook

6.2.1 Metadata

6.2.1.1 Description

Dataset name: results

The dataset has N=790 rows and 34 columns. 0 rows have no missing values on any column.

Metadata for search engines

• Date published: 2021-07-19

x DYUK STUK UKNATION RESIDENT GENDER condition conditionbi genderbi age_cat NATIONALITY INTEREST ATT1 ATT2 ATT3 INTENT1 INTENT2 INTENT3 EXPECT1 EXPECT2 EXPECT3 PERCEPTNUM PERCEPTSCALE CONSTRUAL_1 PERCEPTCHANGE PRECONFORMITY POLITICS AGE attitude_mean intention_mean expect_mean expintent_avg age_cent PERCEPTCHANGE_r comb_future

6.3 Codebook table

name	data_type	ordered	value_labels	n_missing	complete_rate	n_unique	empty	top_counts	min	median	max	mean	sd	whitespace	hist	DYUK	STUK	NATIONALITY	label
<a href=“#DYUK”>DYUK</a>	character	NA	NA	534	0.3240506	256	0	NA	7	NA	898	NA	NA	0	NA	Dynamic UK	NA	NA	NA
<a href=“#STUK”>STUK</a>	character	NA	NA	522	0.3392405	266	0	NA	5	NA	993	NA	NA	0	NA	NA	Static UK	NA	NA
<a href=“#UKNATION”>UKNATION</a>	factor	FALSE	1,<br>2. 2,<br>3. 3,<br>4. 4	21	0.9734177	4	NA	1: 647, 3: 73, 2: 38, 4: 11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#RESIDENT”>RESIDENT</a>	factor	FALSE	1,<br>2. 2	0	1.0000000	2	NA	1: 783, 2: 7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#GENDER”>GENDER</a>	factor	FALSE	Male,<br>2. Female,<br>3. Other	0	1.0000000	3	NA	Fem: 449, Mal: 340, Oth: 1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#condition”>condition</a>	factor	FALSE	Dynamic,<br>2. Static,<br>3. No norm	0	1.0000000	3	NA	Sta: 268, No : 266, Dyn: 256	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#conditionbi”>conditionbi</a>	factor	FALSE	Dynamic,<br>2. Static	266	0.6632911	2	NA	Sta: 268, Dyn: 256	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#genderbi”>genderbi</a>	factor	FALSE	Male,<br>2. Female	1	0.9987342	2	NA	Fem: 449, Mal: 340	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#age_cat”>age_cat</a>	factor	FALSE	Young adults,<br>2. Middle-aged adults,<br>3. Old adults	0	1.0000000	3	NA	Mid: 381, Old: 224, You: 185	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#NATIONALITY”>NATIONALITY</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	3	27.0	183	28.0860759	11.9341320	NA	▇▁▁▁▁	NA	NA	Nationality	NA
<a href=“#INTEREST”>INTEREST</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	3.0	7	3.5417722	1.7999160	NA	▇▅▅▃▅	NA	NA	NA	NA
<a href=“#ATT1”>ATT1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.4607595	1.4303799	NA	▂▂▅▇▅	NA	NA	NA	NA
<a href=“#ATT2”>ATT2</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.5772152	1.3560127	NA	▂▃▆▇▆	NA	NA	NA	NA
<a href=“#ATT3”>ATT3</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.8734177	1.3212420	NA	▁▂▅▇▇	NA	NA	NA	NA
<a href=“#INTENT1”>INTENT1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.1721519	1.7993758	NA	▆▃▅▇▇	NA	NA	NA	NA
<a href=“#INTENT2”>INTENT2</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.1392405	1.7788661	NA	▆▃▅▇▆	NA	NA	NA	NA
<a href=“#INTENT3”>INTENT3</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.3367089	1.8168468	NA	▆▂▃▇▇	NA	NA	NA	NA
<a href=“#EXPECT1”>EXPECT1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.8848101	1.7551365	NA	▇▃▅▇▅	NA	NA	NA	NA
<a href=“#EXPECT2”>EXPECT2</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.8493671	1.7502760	NA	▇▃▅▇▅	NA	NA	NA	NA
<a href=“#EXPECT3”>EXPECT3</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	4.0227848	1.7212568	NA	▆▅▅▇▆	NA	NA	NA	NA
<a href=“#PERCEPTNUM”>PERCEPTNUM</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	3	30.0	60	27.9772152	9.4585742	NA	▁▃▇▂▁	NA	NA	NA	NA
<a href=“#PERCEPTSCALE”>PERCEPTSCALE</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	2.0	5	2.5151899	0.6897013	NA	▁▇▅▁▁	NA	NA	NA	NA
<a href=“#CONSTRUAL_1”>CONSTRUAL_1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	10.5	21	11.1048101	4.1314985	NA	▂▆▇▇▂	NA	NA	NA	NA
<a href=“#PERCEPTCHANGE”>PERCEPTCHANGE</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	3.0	5	2.8227848	0.7809437	NA	▁▃▇▂▁	NA	NA	NA	NA
<a href=“#PRECONFORMITY”>PRECONFORMITY</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	4.1379747	1.1546790	NA	▂▆▇▇▂	NA	NA	NA	NA
<a href=“#POLITICS”>POLITICS</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.4987342	1.2533851	NA	▆▆▇▃▁	NA	NA	NA	NA
<a href=“#AGE”>AGE</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	18	35.0	79	37.3481013	13.5404980	NA	▇▆▅▂▁	NA	NA	NA	NA
<a href=“#attitude_mean”>attitude_mean</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.6371308	1.2576647	NA	▁▂▅▇▃	NA	NA	NA	NA
<a href=“#intention_mean”>intention_mean</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.7	7	4.2160338	1.7679082	NA	▅▃▅▇▆	NA	NA	NA	NA
<a href=“#expect_mean”>expect_mean</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.9189873	1.7166291	NA	▆▅▅▇▃	NA	NA	NA	NA
<a href=“#expintent_avg”>expintent_avg</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.3	7	4.0675105	1.7084989	NA	▅▃▅▇▅	NA	NA	NA	NA
<a href=“#age_cent”>age_cent</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	-19	-2.0	42	0.3356273	13.5404980	NA	▇▆▅▂▁	NA	NA	NA	NA
<a href=“#PERCEPTCHANGE_r”>PERCEPTCHANGE_r</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	3	5.0	7	5.1772152	0.7809437	NA	▁▂▇▃▁	NA	NA	NA	NA
<a href=“#comb_future”>comb_future</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	2	4.5	7	4.6575949	0.8334951	NA	▁▆▇▃▁	NA	NA	NA	NA

JSON-LD metadata

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{
  "name": "results",
  "datePublished": "2021-07-19",
  "description": "The dataset has N=790 rows and 34 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name            |label | n_missing|\n|:---------------|:-----|---------:|\n|DYUK            |NA    |       534|\n|STUK            |NA    |       522|\n|UKNATION        |NA    |        21|\n|RESIDENT        |NA    |         0|\n|GENDER          |NA    |         0|\n|condition       |NA    |         0|\n|conditionbi     |NA    |       266|\n|genderbi        |NA    |         1|\n|age_cat         |NA    |         0|\n|NATIONALITY     |NA    |         0|\n|INTEREST        |NA    |         0|\n|ATT1            |NA    |         0|\n|ATT2            |NA    |         0|\n|ATT3            |NA    |         0|\n|INTENT1         |NA    |         0|\n|INTENT2         |NA    |         0|\n|INTENT3         |NA    |         0|\n|EXPECT1         |NA    |         0|\n|EXPECT2         |NA    |         0|\n|EXPECT3         |NA    |         0|\n|PERCEPTNUM      |NA    |         0|\n|PERCEPTSCALE    |NA    |         0|\n|CONSTRUAL_1     |NA    |         0|\n|PERCEPTCHANGE   |NA    |         0|\n|PRECONFORMITY   |NA    |         0|\n|POLITICS        |NA    |         0|\n|AGE             |NA    |         0|\n|attitude_mean   |NA    |         0|\n|intention_mean  |NA    |         0|\n|expect_mean     |NA    |         0|\n|expintent_avg   |NA    |         0|\n|age_cent        |NA    |         0|\n|PERCEPTCHANGE_r |NA    |         0|\n|comb_future     |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["DYUK", "STUK", "UKNATION", "RESIDENT", "GENDER", "condition", "conditionbi", "genderbi", "age_cat", "NATIONALITY", "INTEREST", "ATT1", "ATT2", "ATT3", "INTENT1", "INTENT2", "INTENT3", "EXPECT1", "EXPECT2", "EXPECT3", "PERCEPTNUM", "PERCEPTSCALE", "CONSTRUAL_1", "PERCEPTCHANGE", "PRECONFORMITY", "POLITICS", "AGE", "attitude_mean", "intention_mean", "expect_mean", "expintent_avg", "age_cent", "PERCEPTCHANGE_r", "comb_future"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "DYUK",
      "description": "Dynamic UK",
      "@type": "propertyValue"
    },
    {
      "name": "STUK",
      "description": "Static UK",
      "@type": "propertyValue"
    },
    {
      "name": "UKNATION",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
      "@type": "propertyValue"
    },
    {
      "name": "RESIDENT",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "GENDER",
      "value": "1. Male,\n2. Female,\n3. Other",
      "@type": "propertyValue"
    },
    {
      "name": "condition",
      "value": "1. Dynamic,\n2. Static,\n3. No norm",
      "@type": "propertyValue"
    },
    {
      "name": "conditionbi",
      "value": "1. Dynamic,\n2. Static",
      "@type": "propertyValue"
    },
    {
      "name": "genderbi",
      "value": "1. Male,\n2. Female",
      "@type": "propertyValue"
    },
    {
      "name": "age_cat",
      "value": "1. Young adults,\n2. Middle-aged adults,\n3. Old adults",
      "@type": "propertyValue"
    },
    {
      "name": "NATIONALITY",
      "description": "Nationality",
      "@type": "propertyValue"
    },
    {
      "name": "INTEREST",
      "@type": "propertyValue"
    },
    {
      "name": "ATT1",
      "@type": "propertyValue"
    },
    {
      "name": "ATT2",
      "@type": "propertyValue"
    },
    {
      "name": "ATT3",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT1",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT2",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT3",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT1",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT2",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT3",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTNUM",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTSCALE",
      "@type": "propertyValue"
    },
    {
      "name": "CONSTRUAL_1",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE",
      "@type": "propertyValue"
    },
    {
      "name": "PRECONFORMITY",
      "@type": "propertyValue"
    },
    {
      "name": "POLITICS",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "attitude_mean",
      "@type": "propertyValue"
    },
    {
      "name": "intention_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expect_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expintent_avg",
      "@type": "propertyValue"
    },
    {
      "name": "age_cent",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE_r",
      "@type": "propertyValue"
    },
    {
      "name": "comb_future",
      "@type": "propertyValue"
    }
  ]
}`